app.py

import gradio as gr

import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM

SAVED_CHECKPOINT = 'mikegarts/distilgpt2-erichmariaremarque'
MIN_WORDS = 80


def get_model():
    model = AutoModelForCausalLM.from_pretrained(SAVED_CHECKPOINT)
    tokenizer = AutoTokenizer.from_pretrained(SAVED_CHECKPOINT)
    return model, tokenizer


def generate(prompt):
    model, tokenizer = get_model()

    input_context = prompt
    input_ids = tokenizer.encode(input_context, return_tensors="pt").to('cuda')

    outputs = model.generate(
        input_ids=input_ids, 
        max_length=100, 
        temperature=0.7, 
        num_return_sequences=3, 
        do_sample=True,
        # forced_eos_token_id=tokenizer.encode('.')[0]
    )

    return tokenizer.decode(outputs[0], skip_special_tokens=True).rsplit('.', 1)[0] + '.'


def predict(prompt):
    return generate(prompt=prompt)


title = "What would Remarques say?"
description = """
The bot was trained to complete your prompt as if it was a begining of a paragraph of Remarque's book.
<img src="https://upload.wikimedia.org/wikipedia/commons/1/10/Bundesarchiv_Bild_183-R04034%2C_Erich_Maria_Remarque_%28cropped%29.jpg" align=center width=200px>
"""

gr.Interface(
    fn=predict,
    inputs="textbox",
    outputs="text",
    title=title,
    description=description,
    examples=[["I was drinking because"], ["Who is Karl for me?"]]
).launch(debug=True)